This project, submitted as a multi-center U19 application on behalf of the INDIGO (Immunogenetics of Neurological DIseases working GrOup) consortium, will identify and characterize the repertoire of HLA and KIR genes and alleles that predispose to neurological diseases (multiple sclerosis, neuromyelitis optica, myasthenia gravis, Parkinson's disease, and schizophrenia). This Project includes 2 independent, yet interactive components supported by a common administrative, data management, and bio-repository infrastructure. Project 1 proposes a comprehensive sequence variation screening in nine HLA loci and association analysis with disease risk. Project 2 will assess the distribution of KIR-associated variants, KIR copy number, and KIR promoter polymorphisms in the same diseases. This is a large and complex grant that spans multiple laboratories at different sites in the US, Europe, and Japan and requires a significant investment of coordination and administrative time by the Principal Investigators, with clear lines of communication, responsibilities, and authority. The Administrative Core will be co-located at the UCSF Department of Neurology and Stanford Histocompatibility, Immunogenetics and Disease Profiling Laboratory. In addition to centralized administration and budgetary oversight, the main goal of the Core is to promote the productive and collegial interaction of all participating investigators, fellows, students, and staff. The Administrative Core will be responsible for all aspects of the project including coordination of experimental and support activities, oversight of budgeting, supervision of cores and projects, scheduling of conference calls, meetings and other interactions, and communications with NINDS, including preparation of progress reports. The geographic proximity between Stanford and UCSF, together with the complementary expertise of the PIs and track record of collaborations warrant an effective, non-redundant partnership in the co-management of the Administrative Core and program. By coordinating and collaborating on data management, common genotyping technologies, and statistical analysis, this group will at once attain cohesive, innovative, and costeffective research.